Adaptive Bounding Box Selection for Vehicle Perception
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Solution Overview
Problem
Existing vehicle perception systems face challenges in balancing computational efficiency and accuracy when representing objects in a surrounding environment, particularly in complex scenarios, as they often rely on a single type of representation that is either inefficient or inaccurate.
Innovation Solution
A system that dynamically selects between two-dimensional and three-dimensional bounding boxes based on contextual information, such as the vehicle's speed, presence of other objects, and processing load, to adapt representation methods according to the specific circumstances, thereby improving both accuracy and computational efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If three-dimensional bounding boxes are used to represent objects, then measurement precision is improved, but use of energy by moving object increases
Solution Approach 1:
The system dynamically adapts the bounding box representation type (2D or 3D) based on real-time driving context, object characteristics, and sensor data quality. This dynamic selection allows the system to use computationally intensive 3D bounding boxes only when necessary for accurate perception, while using simpler 2D bounding boxes in routine conditions, thereby optimizing the balance between measurement precision and computational energy consumption.
Solution Approach 2:
The system changes the parameter of bounding box dimensionality (from 2D to 3D) based on specific conditions such as object distance, relative velocity, and scene complexity. This parameter adaptation enables the system to achieve high measurement precision for critical objects while maintaining lower computational energy consumption for less critical scenarios.
2Device complexity
If a single type of representation is used for all objects, then device complexity is reduced, but adaptability or versatility worsens
Solution Approach 1:
The system implements dynamic representation selection that adapts to different driving contexts, object types, and environmental conditions. By automatically selecting between 2D and 3D bounding boxes based on real-time requirements, the system achieves high adaptability without requiring manual configuration or complex decision-making processes, effectively balancing adaptability with operational simplicity.
Solution Approach 2:
The system autonomously determines the appropriate bounding box representation type based on sensor data quality, object characteristics, and scene context without external intervention. This self-service capability allows the system to adapt its representation strategy to different circumstances automatically, achieving high versatility while maintaining simple operation and reducing the need for complex external control mechanisms.
Data Source
AI summary
Systems, methods, and other embodiments described herein relate to representing identified objects in a surrounding environment of a vehicle. In one embodiment, a method includes, in response to acquiring information including sensor data and operating data in a vehicle, setting a parameter for analyzing the sensor data according to the information to select between 3D bounding boxes and 2D bounding boxes for representing identified objects in a surrounding environment of the vehicle. The method includes analyzing the sensor data according to the parameter. The method includes providing an electronic output in the vehicle according to the parameter.